Method and system for online control of an electric dust precipitation system
By using online control methods and systems, and through real-time parameter simulation and path fitting optimization, the problems of insufficient adjustment accuracy and intelligence of electrostatic precipitator systems have been solved, achieving efficient self-regulation and reducing the cost of manual supervision.
CN117244690BActive Publication Date: 2026-05-19浙江菲达环保科技股份有限公司
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Patent Information
- Application Number
- CN202311429152.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-10-30
- Publication Date
- 2026-05-19
- Estimated Expiration
- 2043-10-30
AI Technical Summary
Technical Problem
The existing electrostatic precipitator systems lack precision and intelligence, making it impossible to find the most efficient adjustment solution, and the cost of manual monitoring is high.
Method used
By collecting real-time operating parameters of the electrostatic precipitator system, simulating operating conditions, constructing a fitting path, and establishing an optimization problem, the fitting parameters are adaptively adjusted with dust removal efficiency as the optimization objective, and control commands are generated to achieve self-regulation.
Benefits of technology
It achieves precise control of the electrostatic precipitator system, improves the objectivity and intelligence of adjustments, reduces manual intervention, and lowers costs.
✦ Generated by Eureka AI based on patent content.
Abstract
The embodiment of the present application provides an online control method and system of an electric dust removal system, and belongs to the technical field of electric dust removal. The method comprises the following steps: collecting real-time operation parameters of the electric dust removal system, and simulating operation conditions based on the real-time operation parameters; determining condition fitting parameters of each preset fitting path based on the simulated conditions; taking dust removal efficiency as an optimization target, taking each fitting path as an optimization variable to construct an optimization problem; adaptively adjusting the condition fitting parameters of each preset fitting path, determining an optimization path based on the linkage condition of the optimization problem, and determining target parameters of the optimization path based on the optimization problem; and generating and executing corresponding control instructions based on the target parameters of the optimization path. The present application scheme ensures accuracy while improving the intelligence of the system when performing electric dust removal efficiency control.
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